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	Wyjaśnialny DBSCAN	Grupowanie K-średnich (K-means Clustering)
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1996 (DBSCAN); 2010s (XAI integration)	1967 (formalized 1982)
Twórca	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	MacQueen, J. B.; Lloyd, S. P.
Typ	Unsupervised clustering with post-hoc interpretability	Partitional clustering
Źródło pierwotne	Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Inne nazwy	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Pokrewne	5	4
Podsumowanie	Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.